Climate Finance: Mapping Air Pollution and Finance Market in Time Series

Zheng Fang,Jianying Xie,Ruiming Peng,Sheng Wang

doi:10.3390/econometrics9040043

Abstract

Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM2.5, PM10, SO2, NO2, CO, and O3 are the target pollutant in the atmosphere and we use a deep neural network to enhance the regression analysis in order to investigate the relationship between air pollution and stock prices of the targeted manufacturer. We also conduct time series analysis based on air pollution and heavy industry manufacturing in China, as the country is facing serious air pollution problems. Our study uses Convolutional-Long Short Term Memory in 2 Dimension (ConvLSTM2D) to extract the features from air pollution and enhance the time series regression in the financial market. The main contribution in our paper is discovering a feature term that impacts the stock price in the financial market, particularly for the companies that are highly impacted by the local environment. We offer a higher accurate model than the traditional time series in the stock price prediction by considering the environmental factor. The experimental results suggest that there is a negative linear relationship between air pollution and the stock market, which demonstrates that air pollution has a negative effect on the financial market. It promotes the manufacturer’s improving their emission recycling and encourages them to invest in green manufacture—otherwise, the drop in stock price will impact the company funding process.

Highlights

Growing industrialization bring serious air pollution in emerging economic entities such as China and India
ConvLSTM2D was suggested for mapping the relationship between air pollution and the stock market since it yielded accurate results in the rolling forecast in most cases, better than the time series model without feature term AP1 from air pollution data, which indicates the nonlinear relationship between air pollution and stock price
The experiment suggests that time series models with environmental factors have lower mean squared error and other evaluation metrics

Summary

Introduction

Growing industrialization bring serious air pollution in emerging economic entities such as China and India. Paris Climate Accords were signed by 196 parties around the world in order to control the rapid exacerbation of climate change. The concentration of carbon dioxide in Earth’s atmosphere is rapidly rising and is more than 420 parts per million (ppm) according to NASA data. This requires significant action to contain the atmospheric carbon dioxide, which benefits humanity as a whole. Air pollution plays an important role in the area of climate finance because it determines the concentration of carbon dioxide in the Earth’s atmosphere; carbon dioxide and other pollutants block off the interaction between ultraviolet rays and photosynthesis, which exacerbate global warming

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